Jobs at Burq, Inc.

Build and own backend services, web applications, REST APIs, webhooks, and enterprise integrations. Lead customer integrations from scoping through launch, improve the operator dashboard, and maintain production reliability through monitoring, incident response, and root-cause analysis. Collaborate with product, success, and customer engineering teams while contributing to code reviews, technical design, mentoring, security, and AI-assisted development.

Own post-sale relationships with merchant and enterprise customers from onboarding through renewal and expansion. Manage account health, retention, QBRs, churn prevention, adoption, and growth while reporting ARR, churn, and NPS. Serve as the customer voice internally, coordinate with cross-functional teams, support product feedback, and build scalable success processes. Use AI tools daily for reporting, account analysis, documentation, and issue triage. Logistics, API integration, and enterprise account experience are required.

Owner of enterprise customer technical and commercial health: debug integrations, trace API/webhook failures, build dashboards with SQL, lead QBRs and renewals, drive expansion and product adoption, create runbooks, and relay technical feedback to Product and Engineering.

Own technical pre-sales for enterprise delivery deals: run discovery, build tailored demos and POCs, guide engineering through API integrations to first live orders, produce integration specs, respond to RFPs/security questionnaires, and hand off to post-sales while informing product roadmap.

Lead finance and operations at a high-growth marketplace startup: own budgeting, forecasting, capital allocation, revenue strategy, FP&A, accounting operations, audit readiness, fundraising diligence, cap table and scenario modeling, and partner with founders on company-wide operational decisions.

Lead and build Burq's data and ML platform: own warehouse, ingestion, semantic layer, and BI; architect and ship forecasting, optimization, matching, and reliability models; implement feature pipelines, training/serving, and monitoring; drive AI agent initiatives; hire and grow a data/ML team; set governance, intake processes, and partner with product, engineering, and commercial stakeholders to align models with business impact.